The following code:
import numpy as np
import tensorflow as tf
from sklearn.model_selection import train_test_split
import pandas as pd
# DATA PREPARE
df = pd.read_csv('housing.csv')
df = df.dropna()
print(df.head)
print(df.describe())
print(df.info())
# NORMALIZATION
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
scaler.fit(df[['housing_median_age', 'total_rooms', 'total_bedrooms', 'population', 'households', 'median_income',
'median_house_value']])
df_scaled_cols = scaler.transform(df[['housing_median_age', 'total_rooms', 'total_bedrooms',
'population', 'households', 'median_income', 'median_house_value']])
df_scaled_cols = pd.DataFrame(data=df_scaled_cols, columns=['housing_median_age', 'total_rooms', 'total_bedrooms',
'population', 'households', 'median_income',
'median_house_value'])
df = pd.concat([df_scaled_cols, df['ocean_proximity']], axis=1)
# DATAFRAME INTO X AND Y -> TRAIN TEST SPLIT
x_data = df[['housing_median_age', 'total_rooms', 'total_bedrooms', 'population', 'households', 'median_income',
'ocean_proximity']]
y_label = df['median_house_value']
X_train, X_test, y_train, y_test = train_test_split(x_data, y_label, test_size=0.3)
# FEATURE COLUMNS FROM DATA
m_age = tf.feature_column.numeric_column('housing_median_age')
rooms = tf.feature_column.numeric_column('total_rooms')
bedrooms = tf.feature_column.numeric_column('total_bedrooms')
population = tf.feature_column.numeric_column('population')
households = tf.feature_column.numeric_column('households')
income = tf.feature_column.numeric_column('median_income')
ocean = tf.feature_column.categorical_column_with_hash_bucket('ocean_proximity', hash_bucket_size=10)
embedded_ocean = tf.feature_column.embedding_column(ocean, dimension=4)
feat_cols = [m_age, rooms, bedrooms, population, households, income, embedded_ocean]
# 3 INPUT FUNCTIONS
train_input_func = tf.estimator.inputs.pandas_input_fn(x=X_train, y=y_train, batch_size=10, num_epochs=1000,
shuffle=True)
test_input_func = tf.estimator.inputs.pandas_input_fn(x=X_test, y=y_test, batch_size=10, num_epochs=1, shuffle=False)
predict_input_func = tf.estimator.inputs.pandas_input_fn(x=X_test, batch_size=10, num_epochs=1, shuffle=False)
# DNN_Reg MODEL
dnn_model = tf.estimator.DNNRegressor(hidden_units=[10,10,10], feature_columns=feat_cols)
dnn_model.train(input_fn=train_input_func, steps=1000)
Causes the error:
Traceback (most recent call last): File "C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\client\session.py", line 1278, in _do_call return fn(*args) File "C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\client\session.py", line 1263, in _run_fn options, feed_dict, fetch_list, target_list, run_metadata) File "C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\client\session.py", line 1350, in _call_tf_sessionrun run_metadata) tensorflow.python.framework.errors_impl.InternalError: Unable to get element as bytes.
During handling of the above exception, another exception occurred:
Traceback (most recent call last): File "C:/Users/Admin/Documents/PycharmProjects/TF_Regression_Project/project.py", line 69, in dnn_model.train(input_fn=train_input_func, steps=1000) File "C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\estimator\estimator.py", line 376, in train loss = self._train_model(input_fn, hooks, saving_listeners) File "C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\estimator\estimator.py", line 1145, in _train_model return self._train_model_default(input_fn, hooks, saving_listeners) File "C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\estimator\estimator.py", line 1173, in _train_model_default saving_listeners) File "C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\estimator\estimator.py", line 1451, in _train_with_estimator_spec _, loss = mon_sess.run([estimator_spec.train_op, estimator_spec.loss]) File "C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\training\monitored_session.py", line 695, in exit self._close_internal(exception_type) File "C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\training\monitored_session.py", line 732, in _close_internal self._sess.close() File "C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\training\monitored_session.py", line 980, in close self._sess.close() File "C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\training\monitored_session.py", line 1124, in close ignore_live_threads=True) File "C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\training\coordinator.py", line 389, in join six.reraise(*self._exc_info_to_raise) File "C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site-packages\six.py", line 692, in reraise raise value.with_traceback(tb) File "C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\estimator\inputs\queues\feeding_queue_runner.py", line 94, in _run sess.run(enqueue_op, feed_dict=feed_dict) File "C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\client\session.py", line 877, in run run_metadata_ptr) File "C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\client\session.py", line 1100, in _run feed_dict_tensor, options, run_metadata) File "C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\client\session.py", line 1272, in _do_run run_metadata) File "C:\Users\Admin\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\client\session.py", line 1291, in _do_call raise type(e)(node_def, op, message) tensorflow.python.framework.errors_impl.InternalError: Unable to get element as bytes.
What is wrong inside?
